Aiming at the problem that in detection of flying objects

due to the complex background of target and low recognition accuracy caused by different postures

a flying object recognition algorithm based on improved Faster R-CNN is proposed.The recognition algorithm uses the achromatic refracting telescope ETX80 and PC to construct the flying object recognition system.A dataset containing three kinds of flying objects of UAV

airplanes and birds is constructed

and the dataset is marked and divided.ResNet101 deep residual network is used to extract the image features

and input constructed deep learning network model for training.The network model includes the regional proposal network

the ROI pooling layer and the classification layer.The test results show that the method can achieve good results in the identification of three types of airborne objects such as uavs

airplanes and birds in different backgrounds.The mean average precision is 96.7%

which is 3.1% higher than that of Faster R-CNN algorithm.